Skilled Nursing Facilities – Try this tool to identify those at high risk of readmission.
The HOSPITAL Score for Readmission, developed by Donzé et al., is a validated prediction tool to identify patients at a high risk of potentially avoidable hospital readmission. This resource is a virtual calculator from MDCalc to use the HOSPITAL Score to predict 30-day potentially avoidable hospital readmissions.
Additional Resources:
Potentially Avoidable 30-Day Hospital Readmissions in Medical Patients: Derivation and Validation of a Prediction Model by Jacques Donzé, MD, MSc; Drahomir Aujesky, MD, MSc; Deborah Williams, MHA; et al
The objective of this retrospective cohort study was to derive and internally validate a prediction model for potentially avoidable 30-day hospital readmissions in medical patients using administrative and clinical data readily available prior to discharge. It concluded that this simple prediction model identifies before discharge the risk of potentially avoidable 30-day readmission in medical patients. This score has potential to easily identify patients who may need more intensive transitional care interventions.
Validation of the HOSPITAL Score for 30-Day All-Cause Readmissions of Patients Discharged to Skilled Nursing Facilities by Luke Kim, MD; Lei Kou; Barbara Messinger-Rapport; Michael Rothberg
The objective of this retrospective cohort study was to validate the HOSPITAL score for predicting 30-day all-cause readmission rates in a cohort of medical patients discharged to skilled nursing facilities (SNFs). It concluded that among patients discharged to a skilled nursing facility (SNF), the HOSPITAL score may be used to identify those at highest risk of readmission within 30 days.
Predicting Hospital Readmissions from Skilled Nursing Facilities | Original study validates a good model: HOSPITAL score
This article describes the study by Dr. Kim above. They concluded that the HOSPITAL Score successfully stratified patients regarding risk of all-cause 30-day readmissions and identified clinically meaningful differences between low- and high-risk patients.